Bottom Line:
Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination.Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved.The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.

ABSTRACTWireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.

Mentions:
A 30 m tall high-rise building model was selected as the object of study. We assume that the building is located in a temperate city and has 10 internal layers, each with the same style, height and pattern. There are two stairwells, one elevator, and the atrium going through layers 1 to 10. There are windows to the outside around each layer in the architecture. The initial room temperature was 20 °C. The temperature universe is between 0 and 60 °C. The universe of the flue gas dimming extent parameter is between 0% and 100%. The wind from the air inlet at the main entrance can reach a speed of 10 m/s. In addition, 100 WSN fire detector nodes were set up in the building. All these nodes have the same configuration specifications, and the sensors are based on the ZigBee wireless communication protocol [37,38]. Furthermore, the SHT71 temperature detector, MQ-2 photoelectric smoke fire detector, and two CC2420 radio frequency modules, which are shown in Figure 7 and Figure 8, were adopted to perform the data acquisition. The building model is shown in Figure 9. The symbol stands the vertical position of sensor nodes on each layer.

Mentions:
A 30 m tall high-rise building model was selected as the object of study. We assume that the building is located in a temperate city and has 10 internal layers, each with the same style, height and pattern. There are two stairwells, one elevator, and the atrium going through layers 1 to 10. There are windows to the outside around each layer in the architecture. The initial room temperature was 20 °C. The temperature universe is between 0 and 60 °C. The universe of the flue gas dimming extent parameter is between 0% and 100%. The wind from the air inlet at the main entrance can reach a speed of 10 m/s. In addition, 100 WSN fire detector nodes were set up in the building. All these nodes have the same configuration specifications, and the sensors are based on the ZigBee wireless communication protocol [37,38]. Furthermore, the SHT71 temperature detector, MQ-2 photoelectric smoke fire detector, and two CC2420 radio frequency modules, which are shown in Figure 7 and Figure 8, were adopted to perform the data acquisition. The building model is shown in Figure 9. The symbol stands the vertical position of sensor nodes on each layer.

Bottom Line:
Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination.Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved.The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.

ABSTRACTWireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.